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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?, , , , , , , , , and 29 other author(s). Human Brain Mapping, 41 (14): 3839-3854 (July 2020)Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment., , , , , and . Neuroinformatics, 17 (4): 547-561 (2019)Dynamic reconfiguration of human brain functional networks through neurofeedback., , , , , , , and . NeuroImage, (2013)Decomposing dynamic functional connectivity onto phase-dependent eigenconnectivities using the Hilbert transform., , , and . ISBI, page 38-41. IEEE, (2015)MCI Identification by Joint Learning on Multiple MRI Data., , , , , , and . MICCAI (2), volume 9350 of Lecture Notes in Computer Science, page 78-85. Springer, (2015)Detecting Perfusion Pattern Based on the Background Low-Frequency Fluctuation in Resting-State Functional Magnetic Resonance Imaging Data and Its Influence on Resting-State Networks: An Iterative Postprocessing Approach., , , , , and . Brain Connect., 7 (10): 627-634 (2017)Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , and 38 other author(s). NeuroImage, (August 2021)Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training., , , , and . IEEE Trans. Biomed. Eng., 64 (6): 1228-1237 (2017)Closed-loop brain training: the science of neurofeedback, , , , , , , , , and 2 other author(s). Nature Reviews Neuroscience, 18 (2): 86--100 (December 2016)Predicting individual scores from resting state fMRI using partial least squares regression., , , , , , , , and . ISBI, page 1311-1314. IEEE, (2016)